Fetal Heart Rate Deceleration Detection Using a Discrete Cosine Transform Implementation of Singular Spectrum Analysis

2007 ◽  
Vol 46 (02) ◽  
pp. 196-201
Author(s):  
D. Precup ◽  
E. Hamilton ◽  
R. Kearney ◽  
P. Warrick
Keyword(s):  
Heart Rate ◽  
Discrete Cosine Transform ◽  
Spectrum Analysis ◽  
Change Point ◽  
Fetal Heart Rate ◽  
Fetal Heart ◽  
Singular Spectrum Analysis ◽  
Change Point Detection ◽  
Cosine Transform ◽  
Point Detection

Summary Objectives : To develop a singular-spectrum analysis (SSA) based change-point detection algorithm applicable to fetal heart rate (FHR) monitoring to improve the detection of deceleration events. Methods : We present a method for decomposing a signal into near-orthogonal components via the discrete cosine transform (DCT) and apply this in a novel online manner to change-point detection based on SSA. The SSA technique forms models of the underlying signal that can be compared over time; models that are sufficiently different indicate signal change points. To adapt the algorithm to deceleration detection where many successive similar change events can occur, we modify the standard SSA algorithm to hold the reference model constant under such conditions, an approach that we term “base-hold SSA”. The algorithm is applied to a database of 15 FHR tracings that have been preprocessed to locate candidate decelerations and is compared to the markings of an expert obstetrician. Results : Of the 528 true and 1285 false decelerations presented to the algorithm, the base-hold approach improved on standard SSA, reducing the number of missed decelerations from 64 to 49 (21.9%) while maintaining the same reduction in false-positives (278). Conclusions : The standard SSA assumption that changes are infrequent does not apply to FHR analysis where decelerations can occur successively and in close proximity; our base-hold SSA modification improves detection of these types of event series.

scholarly journals Automatic Near Real-Time Outlier Detection and Correction in Cardiac Interbeat Interval Series for Heart Rate Variability Analysis: Singular Spectrum Analysis-Based Approach (Preprint)

2018 ◽  
Author(s):  
Michael Lang
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Real Time ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Detection Performance ◽  
Change Point Detection ◽  
Heart Rate Variability Analysis ◽  
Singular Spectrum ◽  
Ectopic Beats

BACKGROUND Heart rate variability (HRV) is derived from the series of R-R intervals extracted from an electrocardiographic (ECG) measurement. Ideally all components of the R-R series are the result of sinoatrial node depolarization. However, the actual R-R series are contaminated by outliers due to heart rhythm disturbances such as ectopic beats, which ought to be detected and corrected appropriately before HRV analysis. OBJECTIVE We have introduced a novel, lightweight, and near real-time method to detect and correct anomalies in the R-R series based on the singular spectrum analysis (SSA). This study aimed to assess the performance of the proposed method in terms of (1) detection performance (sensitivity, specificity, and accuracy); (2) root mean square error (RMSE) between the actual N-N series and the approximated outlier-cleaned R-R series; and (3) how it benchmarks against a competitor in terms of the relative RMSE. METHODS A lightweight SSA-based change-point detection procedure, improved through the use of a cumulative sum control chart with adaptive thresholds to reduce detection delays, monitored the series of R-R intervals in real time. Upon detection of an anomaly, the corrupted segment was substituted with the respective outlier-cleaned approximation obtained using recurrent SSA forecasting. Next, N-N intervals from a 5-minute ECG segment were extracted from each of the 18 records in the MIT-BIH Normal Sinus Rhythm Database. Then, for each such series, a number (randomly drawn integer between 1 and 6) of simulated ectopic beats were inserted at random positions within the series and results were averaged over 1000 Monte Carlo runs. Accordingly, 18,000 R-R records corresponding to 5-minute ECG segments were used to assess the detection performance whereas another 180,000 (10,000 for each record) were used to assess the error introduced in the correction step. Overall 198,000 R-R series were used in this study. RESULTS The proposed SSA-based algorithm reliably detected outliers in the R-R series and achieved an overall sensitivity of 96.6%, specificity of 98.4% and accuracy of 98.4%. Furthermore, it compared favorably in terms of discrepancies of the cleaned R-R series compared with the actual N-N series, outperforming an established correction method on average by almost 30%. CONCLUSIONS The proposed algorithm, which leverages the power and versatility of the SSA to both automatically detect and correct artifacts in the R-R series, provides an effective and efficient complementary method and a potential alternative to the current manual-editing gold standard. Other important characteristics of the proposed method include the ability to operate in near real-time, the almost entirely model-free nature of the framework which does not require historical training data, and its overall low computational complexity.

scholarly journals Adaptive change point detection algorithm for heart rate in children

2005 ◽  
Vol 52 (S1) ◽  
pp. A172-A172
Author(s):  
Mark Ansermino ◽  
Ping Yang ◽  
Joanne Lim ◽  
Guy Dumont ◽  
Craig R Ries
Keyword(s):  
Heart Rate ◽  
Change Point ◽  
Detection Algorithm ◽  
Adaptive Change ◽  
Change Point Detection ◽  
Point Detection
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